Evaluative skill in the creative process: A cross-cultural study

Guo, Yawei; Lin, Shengjie; Williams, Zachary J.; Zeng, Yuntao; Clark, Lili Q. Cole

doi:10.1016/j.tsc.2023.101240

Cited by 3 publications

(1 citation statement)

References 91 publications

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“…There are emerging cross‐cultural studies comparing East Asian and Western populations on creativity, of which most employed DT tests as the creativity indicator (Guo et al., 2021; Guo, Lin, Williams, Zeng, & Clark, 2023; Niu & Sternberg, 2002). In general, there appears more evidence supporting the higher creativity level of Western respondents (see Ng, 2001 and Shao et al., 2019 for a review; examples: Deng, Wang, & Zhao, 2016; Ivancovsky, Shamay‐Tsoory, Lee, Morio, & Kurman, 2019; Saeki, Fan, & Dusen, 2001), although there is also opposing evidence (e.g., Ball & Torrance, 1978; Saad, Cleveland, & Ho, 2015).…”

Section: Divergent Thinking and Creative Self‐efficacy In Cross‐cultu...mentioning

confidence: 99%

Creative Potential and Creative Self‐Belief: Measurement Invariance in Cross‐Cultural Contexts

Guo,

Lin,

Williams

et al. 2024

Journal of Creative Behavior

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Cross‐cultural studies on creativity, mainly focusing on the creative potential aspect (e.g., divergent thinking), are emerging in recent years. However, the creative self‐belief (e.g., creative self‐efficacy) aspect of creativity is under‐researched cross‐culturally. Moreover, studies that address the measurement invariance of creativity assessments to ensure the measures' sound psychometric properties cross‐culturally are rare. Thus, we aimed to address (a) the measurement invariance and (b) the mean comparisons of divergent thinking and creative self‐efficacy between American and Chinese adults in two studies. Study 1 investigated four divergent thinking (DT) tests (Line Meanings, Uses, Instances, and Consequences) between American (n = 341) and Chinese (n = 345) college students. Multi‐group confirmatory factor analyses (CFA) for fluency‐supported scalar invariance based on a three‐factor model (removing one Instances item) and those on originality‐supported partial scalar invariance (freeing Uses intercepts) based on a three‐factor model. American respondents exhibited higher latent means on fluency and originality compared to their Chinese counterparts. Study 2 investigated a creative self‐efficacy (CSE) scale between American (n = 302) and Chinese (n = 316) college students. Multi‐group CFA supported scalar invariance based on a one‐factor model (removing one item). Latent mean comparisons showed no difference in CSE across the two groups.

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Section: Divergent Thinking and Creative Self‐efficacy In Cross‐cultu...mentioning

confidence: 99%

Creative Potential and Creative Self‐Belief: Measurement Invariance in Cross‐Cultural Contexts

Guo,

Lin,

Williams

et al. 2024

Journal of Creative Behavior

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Family context of mindset matters: Students’ perceptions of parent and sibling math mindsets predict their math motivation, behavior, and affect

Lin

Muenks

2023

Applied Developmental Science

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Dynamics of automatized measures of creativity: mapping the landscape to quantify creative ideation

Ul Haq,

Pifarré

2023

Front. Educ.

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The growing body of creativity research involves Artificial Intelligence (AI) and Machine learning (ML) approaches to automatically evaluating creative solutions. However, numerous challenges persist in evaluating the creativity dimensions and the methodologies employed for automatic evaluation. This paper contributes to this research gap with a scoping review that maps the Natural Language Processing (NLP) approaches to computations of different creativity dimensions. The review has two research objectives to cover the scope of automatic creativity evaluation: to identify different computational approaches and techniques in creativity evaluation and, to analyze the automatic evaluation of different creativity dimensions. As a first result, the scoping review provides a categorization of the automatic creativity research in the reviewed papers into three NLP approaches, namely: text similarity, text classification, and text mining. This categorization and further compilation of computational techniques used in these NLP approaches help ameliorate their application scenarios, research gaps, research limitations, and alternative solutions. As a second result, the thorough analysis of the automatic evaluation of different creativity dimensions differentiated the evaluation of 25 different creativity dimensions. Attending similarities in definitions and computations, we characterized seven core creativity dimensions, namely: novelty, value, flexibility, elaboration, fluency, feasibility, and others related to playful aspects of creativity. We hope this scoping review could provide valuable insights for researchers from psychology, education, AI, and others to make evidence-based decisions when developing automated creativity evaluation.

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Evaluative skill in the creative process: A cross-cultural study

Cited by 3 publications

References 91 publications

Creative Potential and Creative Self‐Belief: Measurement Invariance in Cross‐Cultural Contexts

Creative Potential and Creative Self‐Belief: Measurement Invariance in Cross‐Cultural Contexts

Family context of mindset matters: Students’ perceptions of parent and sibling math mindsets predict their math motivation, behavior, and affect

Dynamics of automatized measures of creativity: mapping the landscape to quantify creative ideation

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